Personalized Genomic Medicine: A Future Prerequisite for the Prevention of Coronary Artery Disease
Bibliographic record
Abstract
Within the next 10-15 years, medicine will be personalized in large part on the basis of the individual's genomic variants. Coronary artery disease remains the number one cause of morbidity and mortality in the Western world and is predicted to become the number one cause worldwide by 2010. It has been stated that treating the risk factors of coronary artery disease has made it a preventable disease that should be eliminated in the 21st century. It is postulated that about 50% of susceptibility to coronary artery disease is genetic, involving known and occult risk factors. Thus, comprehensive prevention will require identification of genetic susceptibility. The recent technology of a chip with 500,000 DNA markers makes genome-wide scanning to identify the genes contributing to coronary artery disease possible. Multislice CT will provide the high-throughput coronary arteriograms required for this research and for prevention in asymptomatic individuals with a family history of heart disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".